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Survey details methodologies for accelerating deep learning on heterogeneous architectures

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive survey of methodologies and tools designed to accelerate deep learning on heterogeneous architectures. It covers hardware-software co-design, automated synthesis, domain-specific compilers, and design space exploration. The review aims to offer a broad perspective on the rapidly evolving field of deep learning accelerators, highlighting technical challenges and future research directions. AI

IMPACT Provides a structured overview of techniques for optimizing AI model performance on diverse hardware.

RANK_REASON The item is a survey paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details methodologies for accelerating deep learning on heterogeneous architectures

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The item is a survey paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Luca Bompani, Luca Benini, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Valeria Cardellin… ·

    A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

    arXiv:2311.17815v3 Announce Type: replace-cross Abstract: Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, lead…

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